{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Credit Card Fraud - Model Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Machine Learning models tuning is a type of optimization problem. We have a set of hyperparameters and we aim to find the right combination of their values which can help us to find either the minimum (eg. loss) or the maximum (eg. accuracy) of a function.\n",
    "\n",
    "Below, is shown a really simple example of how to find the minimum of a function using Scipy. \n",
    "\n",
    "In this Notebook, I will walk you through different techniques commonly used in Machine Learning to optimise Hyperparameters selection to improve accuracy results.\n",
    "\n",
    "**Table of Contents:**\n",
    "1. Manual Search\n",
    "2. Random Search\n",
    "3. Grid Search\n",
    "4. Automated Hyperparameter Tuning (Bayesian Optimization, Genetic Algorithms)\n",
    "5. Artificial Neural Networks (ANNs) Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Global minima (-20-15) at: 0.013750000000006354\n",
      "Local minimum (5-15) at: 12.078382194916786\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Source: https://scipy-lectures.org/intro/scipy/auto_examples/plot_optimize_example2.html\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import optimize\n",
    "\n",
    "# Creating a function to examine\n",
    "x = np.arange(-20, 15, 0.3)\n",
    "def f(x):\n",
    "    return x**2 - (5*x)/7 - 50*np.cos(x)\n",
    "\n",
    "# Global optimization\n",
    "grid = (-20, 15, 0.3)\n",
    "xmin_global = optimize.brute(f, (grid, ))\n",
    "print(\"Global minima (-20-15) at: {}\".format(float(xmin_global)))\n",
    "\n",
    "# Constrained optimization\n",
    "xmin_local = optimize.fminbound(f, 5, 15)\n",
    "print(\"Local minimum (5-15) at: {}\".format(xmin_local))\n",
    "\n",
    "# Plotting the function\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "plt.plot(x, f(x), 'b', label=\"f(x)\")\n",
    "\n",
    "# Plotting horizontal line where possible roots can be found \n",
    "plt.axhline(0, color='gray', label=\"Roots Level\")\n",
    "\n",
    "# Plotting the function minima\n",
    "xmins = np.array([xmin_global[0], xmin_local])\n",
    "plt.plot(xmins, f(xmins), 'go', label=\"Minima\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"f(x)\")\n",
    "plt.title(\"Finding the minimum of a function\")\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A machine learning model can be composed by two different types of parameters:\n",
    "- Hyperparamters are all the parameters which can be arbitrarely set by the user before starting training (eg. number of estimators in Random Forest). \n",
    "- Model parameters are instead learned during the model training (eg. weights in Neural Networks, Linear Regression).\n",
    "\n",
    "The model parameters define how to use input data to get a desired output, while the hyperparameters determine how our model is structured in the first place. \n",
    "\n",
    "This can be particularly important when comparing how different Machine Learning models performs on a dataset. In fact, I would be unfair for example to compare an SVM model with the best hyperparameters against a Random Forest model with has not been optimized.\n",
    "\n",
    "I will now walk you through a practical example using Kaggle Credit Card Fraud Dataset. In this case, I decided to use just a subset of the dataset, in order to speed up training times and make sure to achieve a perfect balance between the two different classes. Additionally, just a limited amount of features will be used in order to make the optimization tasks more challenging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/creditcardfraud/creditcard.csv\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "from matplotlib.pyplot import figure\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import os\n",
    "\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.359807</td>\n",
       "      <td>-0.072781</td>\n",
       "      <td>2.536347</td>\n",
       "      <td>1.378155</td>\n",
       "      <td>-0.338321</td>\n",
       "      <td>0.462388</td>\n",
       "      <td>0.239599</td>\n",
       "      <td>0.098698</td>\n",
       "      <td>0.363787</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.018307</td>\n",
       "      <td>0.277838</td>\n",
       "      <td>-0.110474</td>\n",
       "      <td>0.066928</td>\n",
       "      <td>0.128539</td>\n",
       "      <td>-0.189115</td>\n",
       "      <td>0.133558</td>\n",
       "      <td>-0.021053</td>\n",
       "      <td>149.62</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.191857</td>\n",
       "      <td>0.266151</td>\n",
       "      <td>0.166480</td>\n",
       "      <td>0.448154</td>\n",
       "      <td>0.060018</td>\n",
       "      <td>-0.082361</td>\n",
       "      <td>-0.078803</td>\n",
       "      <td>0.085102</td>\n",
       "      <td>-0.255425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.225775</td>\n",
       "      <td>-0.638672</td>\n",
       "      <td>0.101288</td>\n",
       "      <td>-0.339846</td>\n",
       "      <td>0.167170</td>\n",
       "      <td>0.125895</td>\n",
       "      <td>-0.008983</td>\n",
       "      <td>0.014724</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.358354</td>\n",
       "      <td>-1.340163</td>\n",
       "      <td>1.773209</td>\n",
       "      <td>0.379780</td>\n",
       "      <td>-0.503198</td>\n",
       "      <td>1.800499</td>\n",
       "      <td>0.791461</td>\n",
       "      <td>0.247676</td>\n",
       "      <td>-1.514654</td>\n",
       "      <td>...</td>\n",
       "      <td>0.247998</td>\n",
       "      <td>0.771679</td>\n",
       "      <td>0.909412</td>\n",
       "      <td>-0.689281</td>\n",
       "      <td>-0.327642</td>\n",
       "      <td>-0.139097</td>\n",
       "      <td>-0.055353</td>\n",
       "      <td>-0.059752</td>\n",
       "      <td>378.66</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.966272</td>\n",
       "      <td>-0.185226</td>\n",
       "      <td>1.792993</td>\n",
       "      <td>-0.863291</td>\n",
       "      <td>-0.010309</td>\n",
       "      <td>1.247203</td>\n",
       "      <td>0.237609</td>\n",
       "      <td>0.377436</td>\n",
       "      <td>-1.387024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.108300</td>\n",
       "      <td>0.005274</td>\n",
       "      <td>-0.190321</td>\n",
       "      <td>-1.175575</td>\n",
       "      <td>0.647376</td>\n",
       "      <td>-0.221929</td>\n",
       "      <td>0.062723</td>\n",
       "      <td>0.061458</td>\n",
       "      <td>123.50</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.158233</td>\n",
       "      <td>0.877737</td>\n",
       "      <td>1.548718</td>\n",
       "      <td>0.403034</td>\n",
       "      <td>-0.407193</td>\n",
       "      <td>0.095921</td>\n",
       "      <td>0.592941</td>\n",
       "      <td>-0.270533</td>\n",
       "      <td>0.817739</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009431</td>\n",
       "      <td>0.798278</td>\n",
       "      <td>-0.137458</td>\n",
       "      <td>0.141267</td>\n",
       "      <td>-0.206010</td>\n",
       "      <td>0.502292</td>\n",
       "      <td>0.219422</td>\n",
       "      <td>0.215153</td>\n",
       "      <td>69.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
       "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
       "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
       "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
       "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
       "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
       "\n",
       "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
       "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
       "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
       "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
       "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
       "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
       "\n",
       "        V26       V27       V28  Amount  Class  \n",
       "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
       "1  0.125895 -0.008983  0.014724    2.69      0  \n",
       "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
       "3 -0.221929  0.062723  0.061458  123.50      0  \n",
       "4  0.502292  0.219422  0.215153   69.99      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"../input/creditcardfraud/creditcard.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(284807, 31)\n",
      "Index(['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10',\n",
      "       'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20',\n",
      "       'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount',\n",
      "       'Class'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>percent_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Time</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V16</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amount</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V28</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V27</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V26</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V25</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V24</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V23</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V22</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V21</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V20</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V19</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V18</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V17</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V15</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V1</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V14</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V13</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V12</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V11</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V10</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V9</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V8</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V7</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V6</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V5</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V4</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V3</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V2</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Class</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        percent_missing\n",
       "Time                0.0\n",
       "V16                 0.0\n",
       "Amount              0.0\n",
       "V28                 0.0\n",
       "V27                 0.0\n",
       "V26                 0.0\n",
       "V25                 0.0\n",
       "V24                 0.0\n",
       "V23                 0.0\n",
       "V22                 0.0\n",
       "V21                 0.0\n",
       "V20                 0.0\n",
       "V19                 0.0\n",
       "V18                 0.0\n",
       "V17                 0.0\n",
       "V15                 0.0\n",
       "V1                  0.0\n",
       "V14                 0.0\n",
       "V13                 0.0\n",
       "V12                 0.0\n",
       "V11                 0.0\n",
       "V10                 0.0\n",
       "V9                  0.0\n",
       "V8                  0.0\n",
       "V7                  0.0\n",
       "V6                  0.0\n",
       "V5                  0.0\n",
       "V4                  0.0\n",
       "V3                  0.0\n",
       "V2                  0.0\n",
       "Class               0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percent_missing = df.isnull().sum() * 100 / len(df)\n",
    "missing_values = pd.DataFrame({'percent_missing': percent_missing})\n",
    "missing_values.sort_values(by ='percent_missing' , ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(num=None, figsize=(10, 8), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "sns.set(style=\"ticks\")\n",
    "f = sns.countplot(x=\"Class\", data=df, palette=\"bwr\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbdff644e10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(num=None, figsize=(10, 8), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "corr=df.corr()\n",
    "sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(400, 31)\n",
      "(400, 31)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(800, 31)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df[df.Class == 1][0:400]\n",
    "print(df2.shape)\n",
    "df3 = df[df.Class == 0][0:400]\n",
    "print(df3.shape)\n",
    "\n",
    "df = df2.append(df3, ignore_index=True)\n",
    "#df4.head()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time</th>\n",
       "      <th>V1</th>\n",
       "      <th>V2</th>\n",
       "      <th>V3</th>\n",
       "      <th>V4</th>\n",
       "      <th>V5</th>\n",
       "      <th>V6</th>\n",
       "      <th>V7</th>\n",
       "      <th>V8</th>\n",
       "      <th>V9</th>\n",
       "      <th>...</th>\n",
       "      <th>V21</th>\n",
       "      <th>V22</th>\n",
       "      <th>V23</th>\n",
       "      <th>V24</th>\n",
       "      <th>V25</th>\n",
       "      <th>V26</th>\n",
       "      <th>V27</th>\n",
       "      <th>V28</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>406.0</td>\n",
       "      <td>-2.312227</td>\n",
       "      <td>1.951992</td>\n",
       "      <td>-1.609851</td>\n",
       "      <td>3.997906</td>\n",
       "      <td>-0.522188</td>\n",
       "      <td>-1.426545</td>\n",
       "      <td>-2.537387</td>\n",
       "      <td>1.391657</td>\n",
       "      <td>-2.770089</td>\n",
       "      <td>...</td>\n",
       "      <td>0.517232</td>\n",
       "      <td>-0.035049</td>\n",
       "      <td>-0.465211</td>\n",
       "      <td>0.320198</td>\n",
       "      <td>0.044519</td>\n",
       "      <td>0.177840</td>\n",
       "      <td>0.261145</td>\n",
       "      <td>-0.143276</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>472.0</td>\n",
       "      <td>-3.043541</td>\n",
       "      <td>-3.157307</td>\n",
       "      <td>1.088463</td>\n",
       "      <td>2.288644</td>\n",
       "      <td>1.359805</td>\n",
       "      <td>-1.064823</td>\n",
       "      <td>0.325574</td>\n",
       "      <td>-0.067794</td>\n",
       "      <td>-0.270953</td>\n",
       "      <td>...</td>\n",
       "      <td>0.661696</td>\n",
       "      <td>0.435477</td>\n",
       "      <td>1.375966</td>\n",
       "      <td>-0.293803</td>\n",
       "      <td>0.279798</td>\n",
       "      <td>-0.145362</td>\n",
       "      <td>-0.252773</td>\n",
       "      <td>0.035764</td>\n",
       "      <td>529.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4462.0</td>\n",
       "      <td>-2.303350</td>\n",
       "      <td>1.759247</td>\n",
       "      <td>-0.359745</td>\n",
       "      <td>2.330243</td>\n",
       "      <td>-0.821628</td>\n",
       "      <td>-0.075788</td>\n",
       "      <td>0.562320</td>\n",
       "      <td>-0.399147</td>\n",
       "      <td>-0.238253</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.294166</td>\n",
       "      <td>-0.932391</td>\n",
       "      <td>0.172726</td>\n",
       "      <td>-0.087330</td>\n",
       "      <td>-0.156114</td>\n",
       "      <td>-0.542628</td>\n",
       "      <td>0.039566</td>\n",
       "      <td>-0.153029</td>\n",
       "      <td>239.93</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6986.0</td>\n",
       "      <td>-4.397974</td>\n",
       "      <td>1.358367</td>\n",
       "      <td>-2.592844</td>\n",
       "      <td>2.679787</td>\n",
       "      <td>-1.128131</td>\n",
       "      <td>-1.706536</td>\n",
       "      <td>-3.496197</td>\n",
       "      <td>-0.248778</td>\n",
       "      <td>-0.247768</td>\n",
       "      <td>...</td>\n",
       "      <td>0.573574</td>\n",
       "      <td>0.176968</td>\n",
       "      <td>-0.436207</td>\n",
       "      <td>-0.053502</td>\n",
       "      <td>0.252405</td>\n",
       "      <td>-0.657488</td>\n",
       "      <td>-0.827136</td>\n",
       "      <td>0.849573</td>\n",
       "      <td>59.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7519.0</td>\n",
       "      <td>1.234235</td>\n",
       "      <td>3.019740</td>\n",
       "      <td>-4.304597</td>\n",
       "      <td>4.732795</td>\n",
       "      <td>3.624201</td>\n",
       "      <td>-1.357746</td>\n",
       "      <td>1.713445</td>\n",
       "      <td>-0.496358</td>\n",
       "      <td>-1.282858</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.379068</td>\n",
       "      <td>-0.704181</td>\n",
       "      <td>-0.656805</td>\n",
       "      <td>-1.632653</td>\n",
       "      <td>1.488901</td>\n",
       "      <td>0.566797</td>\n",
       "      <td>-0.010016</td>\n",
       "      <td>0.146793</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Time        V1        V2        V3        V4        V5        V6  \\\n",
       "0   406.0 -2.312227  1.951992 -1.609851  3.997906 -0.522188 -1.426545   \n",
       "1   472.0 -3.043541 -3.157307  1.088463  2.288644  1.359805 -1.064823   \n",
       "2  4462.0 -2.303350  1.759247 -0.359745  2.330243 -0.821628 -0.075788   \n",
       "3  6986.0 -4.397974  1.358367 -2.592844  2.679787 -1.128131 -1.706536   \n",
       "4  7519.0  1.234235  3.019740 -4.304597  4.732795  3.624201 -1.357746   \n",
       "\n",
       "         V7        V8        V9  ...       V21       V22       V23       V24  \\\n",
       "0 -2.537387  1.391657 -2.770089  ...  0.517232 -0.035049 -0.465211  0.320198   \n",
       "1  0.325574 -0.067794 -0.270953  ...  0.661696  0.435477  1.375966 -0.293803   \n",
       "2  0.562320 -0.399147 -0.238253  ... -0.294166 -0.932391  0.172726 -0.087330   \n",
       "3 -3.496197 -0.248778 -0.247768  ...  0.573574  0.176968 -0.436207 -0.053502   \n",
       "4  1.713445 -0.496358 -1.282858  ... -0.379068 -0.704181 -0.656805 -1.632653   \n",
       "\n",
       "        V25       V26       V27       V28  Amount  Class  \n",
       "0  0.044519  0.177840  0.261145 -0.143276    0.00      1  \n",
       "1  0.279798 -0.145362 -0.252773  0.035764  529.00      1  \n",
       "2 -0.156114 -0.542628  0.039566 -0.153029  239.93      1  \n",
       "3  0.252405 -0.657488 -0.827136  0.849573   59.00      1  \n",
       "4  1.488901  0.566797 -0.010016  0.146793    1.00      1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(num=None, figsize=(10, 8), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "sns.set(style=\"ticks\")\n",
    "f = sns.countplot(x=\"Class\", data=df, palette=\"bwr\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['Class'], axis = 1).values\n",
    "Y = df['Class']\n",
    "\n",
    "X = StandardScaler().fit_transform(X)\n",
    "\n",
    "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.30, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[116   0]\n",
      " [  0 124]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00       116\n",
      "           1       1.00      1.00      1.00       124\n",
      "\n",
      "    accuracy                           1.00       240\n",
      "   macro avg       1.00      1.00      1.00       240\n",
      "weighted avg       1.00      1.00      1.00       240\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=300).fit(X_Train,Y_Train)\n",
    "predictionforest = model.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbdff415dd8>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(num=None, figsize=(10, 8), dpi=80, facecolor='w', edgecolor='k')\n",
    "\n",
    "feat_importances = pd.Series(model.feature_importances_, index=df.drop(df[['Class']], \n",
    "                                                                       axis=1).columns)\n",
    "feat_importances.nlargest(30).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>V17</th>\n",
       "      <th>V9</th>\n",
       "      <th>V6</th>\n",
       "      <th>V12</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-2.830056</td>\n",
       "      <td>-2.770089</td>\n",
       "      <td>-1.426545</td>\n",
       "      <td>-2.899907</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.599717</td>\n",
       "      <td>-0.270953</td>\n",
       "      <td>-1.064823</td>\n",
       "      <td>-0.503141</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4.781831</td>\n",
       "      <td>-0.238253</td>\n",
       "      <td>-0.075788</td>\n",
       "      <td>-6.560124</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-12.598419</td>\n",
       "      <td>-0.247768</td>\n",
       "      <td>-1.706536</td>\n",
       "      <td>-10.912819</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.739384</td>\n",
       "      <td>-1.282858</td>\n",
       "      <td>-1.357746</td>\n",
       "      <td>-4.609628</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         V17        V9        V6        V12  Class\n",
       "0  -2.830056 -2.770089 -1.426545  -2.899907      1\n",
       "1   0.599717 -0.270953 -1.064823  -0.503141      1\n",
       "2  -4.781831 -0.238253 -0.075788  -6.560124      1\n",
       "3 -12.598419 -0.247768 -1.706536 -10.912819      1\n",
       "4   6.739384 -1.282858 -1.357746  -4.609628      1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['V17', 'V9', 'V6', 'V12','Class']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[['V17', 'V9', 'V6', 'V12']]\n",
    "Y = df['Class']\n",
    "\n",
    "X = StandardScaler().fit_transform(X)\n",
    "\n",
    "X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size = 0.30, random_state = 101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[110   6]\n",
      " [  6 118]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.95      0.95       116\n",
      "           1       0.95      0.95      0.95       124\n",
      "\n",
      "    accuracy                           0.95       240\n",
      "   macro avg       0.95      0.95      0.95       240\n",
      "weighted avg       0.95      0.95      0.95       240\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(random_state= 101).fit(X_Train,Y_Train)\n",
    "predictionforest = model.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))\n",
    "acc1 = accuracy_score(Y_Test,predictionforest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manual Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using Manual Search, we choose some model hyperparameters based on our judgment/experience. We then train the model, evaluate its accuracy and start the process again. This loop is repeated until a satisficatory accuracy is scored."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main parameters used by a Random Forest Classifier are:\n",
    "- criterion = the function used to evaluate the quality of a split.\n",
    "- max_depth = maximum number of levels allowed in each tree.\n",
    "- max_features = maximum number of features considered when splitting a node.\n",
    "- min_samples_leaf = minimum number of samples which can be stored in a tree leaf.\n",
    "- min_samples_split = minimum number of samples necessary in a node to cause node splitting.\n",
    "- n_estimators = number of trees in the ensamble."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[110   6]\n",
      " [  6 118]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.95      0.95       116\n",
      "           1       0.95      0.95      0.95       124\n",
      "\n",
      "    accuracy                           0.95       240\n",
      "   macro avg       0.95      0.95      0.95       240\n",
      "weighted avg       0.95      0.95      0.95       240\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=10, random_state= 101).fit(X_Train,Y_Train)\n",
    "predictionforest = model.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))\n",
    "acc2 = accuracy_score(Y_Test,predictionforest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[114   2]\n",
      " [ 13 111]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.98      0.94       116\n",
      "           1       0.98      0.90      0.94       124\n",
      "\n",
      "    accuracy                           0.94       240\n",
      "   macro avg       0.94      0.94      0.94       240\n",
      "weighted avg       0.94      0.94      0.94       240\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators= 200, max_features = \"log2\", min_samples_leaf = 20,\n",
    "                               random_state= 101).fit(X_Train,Y_Train)\n",
    "predictionforest = model.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Random Search, we create a grid of hyperparameters and train/test our model on just some random combination of these hyperparameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simple Random Search with Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(This same data visualization analysis can be easily repeated using Grid Search and changing the appropriate parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_features': ['auto', 'sqrt', 'log2', None], 'n_estimators': [151, 267, 384, 500, 617, 733, 850, 966, 1083, 1200]}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "random_search = {\n",
    "               'max_features': ['auto', 'sqrt','log2', None],\n",
    "               'n_estimators': list(np.linspace(151, 1200, 10, dtype = int))}\n",
    "\n",
    "print(random_search)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, I additionally decided to perform Cross-Validation on the training set. When performing Machine Learning tasks, we generally divide our dataset in training and test sets. This is done so that to test our model after having trained it to check it's performances when working with unseen data. When using Cross-Validation, we divide our training set in N other partitions to make sure our model is not overfitting our data. \n",
    "\n",
    "One of the most common used Cross-Validation methods is K-Fold Validation. In K-Fold, we divide our dataset in N partitions and then iteratively train our model using N-1 partitions and test it with the left-over partition (at each iteration we change the left-over partition). Once having trained N times our model we then average the training results obtained in each iteration to obtain our overall training performance results.\n",
    "\n",
    "![](http://ethen8181.github.io/machine-learning/model_selection/img/kfolds.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Cross-Validation when implementing Hyperparameters optimization can be really important. In this way, we might avoid using some Hyperparameters which works really good on the training data but not so good with the test data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 40 candidates, totalling 160 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:266: UserWarning: The total space of parameters 40 is smaller than n_iter=80. Running 40 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  % (grid_size, self.n_iter, grid_size), UserWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    5.3s\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:   30.1s\n",
      "[Parallel(n_jobs=-1)]: Done 160 out of 160 | elapsed:  1.4min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=4, error_score='raise-deprecating',\n",
       "                   estimator=RandomForestClassifier(bootstrap=True,\n",
       "                                                    class_weight=None,\n",
       "                                                    criterion='gini',\n",
       "                                                    max_depth=None,\n",
       "                                                    max_features='auto',\n",
       "                                                    max_leaf_nodes=None,\n",
       "                                                    min_impurity_decrease=0.0,\n",
       "                                                    min_impurity_split=None,\n",
       "                                                    min_samples_leaf=1,\n",
       "                                                    min_samples_split=2,\n",
       "                                                    min_weight_fraction_leaf=0.0,\n",
       "                                                    n_estimators='warn',\n",
       "                                                    n_jobs=None,\n",
       "                                                    oob_score=False,\n",
       "                                                    random_state=None,\n",
       "                                                    verbose=0,\n",
       "                                                    warm_start=False),\n",
       "                   iid='warn', n_iter=80, n_jobs=-1,\n",
       "                   param_distributions={'max_features': ['auto', 'sqrt', 'log2',\n",
       "                                                         None],\n",
       "                                        'n_estimators': [151, 267, 384, 500,\n",
       "                                                         617, 733, 850, 966,\n",
       "                                                         1083, 1200]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=101, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=5)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier()\n",
    "model = RandomizedSearchCV(estimator = clf, param_distributions = random_search, n_iter = 80, \n",
    "                               cv = 4, verbose= 5, random_state= 101, n_jobs = -1)\n",
    "model.fit(X_Train,Y_Train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbdff31d198>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = pd.pivot_table(pd.DataFrame(model.cv_results_),\n",
    "    values='mean_test_score', index='param_n_estimators', columns='param_max_features')\n",
    "     \n",
    "sns.heatmap(table)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chaning evaluation metric from Accuracy to Area Under the Curve (AUC)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:266: UserWarning: The total space of parameters 40 is smaller than n_iter=80. Running 40 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  % (grid_size, self.n_iter, grid_size), UserWarning)\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 40 candidates, totalling 160 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    2.3s\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:   27.5s\n",
      "[Parallel(n_jobs=-1)]: Done 160 out of 160 | elapsed:  1.3min finished\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "def auc_metric(true, pred):\n",
    "    auc_score = roc_auc_score(pred, true)    \n",
    "    return auc_score\n",
    "\n",
    "metric = make_scorer(auc_metric, greater_is_better=True)\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "model = RandomizedSearchCV(estimator = clf, param_distributions = random_search,  \n",
    "                               n_iter = 80, cv = 4, verbose= 5, random_state= 101, \n",
    "                               n_jobs = -1, scoring = metric)\n",
    "\n",
    "model.fit(X_Train,Y_Train)\n",
    "\n",
    "n_estimators, max_features = list(np.linspace(151, 1200, 10, dtype = int)), ['auto', 'sqrt','log2', None]\n",
    "\n",
    "res = model.cv_results_['mean_test_score'].reshape(len(n_estimators), len(max_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Using Area Under the Curve instead of Accuracy as evaluation metric')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = pd.pivot_table(pd.DataFrame(model.cv_results_), \n",
    "                       values='mean_test_score', index='param_n_estimators', columns='param_max_features')\n",
    "     \n",
    "sns.heatmap(table)\n",
    "plt.title(\"Using Area Under the Curve instead of Accuracy as evaluation metric\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing how cross validation accuracy changes varying the number of estimators and maximum features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/matplotlib/cbook/__init__.py:424: MatplotlibDeprecationWarning: \n",
      "Passing one of 'on', 'true', 'off', 'false' as a boolean is deprecated; use an actual boolean (True/False) instead.\n",
      "  warn_deprecated(\"2.2\", \"Passing one of 'on', 'true', 'off', 'false' as a \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def search_plot(grid, param1, param2, name1, name2):\n",
    "\n",
    "    grid = grid.cv_results_\n",
    "    scores_mean = grid['mean_test_score']\n",
    "    scores_mean = np.array(scores_mean).reshape(len(param2),len(param1))\n",
    "\n",
    "    figure(num=None, figsize=(10, 8), dpi=80, facecolor='w', edgecolor='k')\n",
    "    for idx, val in enumerate(param2):\n",
    "        plt.plot(param1, scores_mean[idx,:], '-o', label= name2 + ': ' + str(val))\n",
    "\n",
    "    plt.title(\"Random Search Accuracy\", fontsize=15)\n",
    "    plt.xlabel(name1, fontsize=12)\n",
    "    plt.ylabel('Cross-Validation Average Accuracy', fontsize=12)\n",
    "    plt.legend(loc=\"best\", fontsize=12)\n",
    "    plt.grid('on')\n",
    "    \n",
    "search_plot(model, n_estimators, max_features, 'Estimators Number', 'Max Features')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing how validation accuracy changes varying the estimators number (incorporating uncertainty in accuracy)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From: https://matthewbilyeu.com/blog/2019-02-05/validation-curve-plot-from-gridsearchcv-results\n",
    "import numbers\n",
    "\n",
    "def plot_grid_search_validation_curve(grid, param_to_vary, title='Validation Accuracy Curve', ylim=None,\n",
    "                                      xlim=None, log=None):\n",
    "\n",
    "    df_cv_results = pd.DataFrame(grid.cv_results_)\n",
    "    valid_scores_mean = df_cv_results['mean_test_score']\n",
    "    valid_scores_std = df_cv_results['std_test_score']\n",
    "\n",
    "    param_cols = [c for c in df_cv_results.columns if c[:6] == 'param_']\n",
    "    param_ranges = [grid.param_distributions[p[6:]] for p in param_cols]\n",
    "    param_ranges_lengths = [len(pr) for pr in param_ranges]\n",
    "\n",
    "    valid_scores_mean = np.array(valid_scores_mean).reshape(*param_ranges_lengths)\n",
    "    valid_scores_std = np.array(valid_scores_std).reshape(*param_ranges_lengths)\n",
    "\n",
    "    param_to_vary_idx = param_cols.index('param_{}'.format(param_to_vary))\n",
    "\n",
    "    slices = []\n",
    "    for idx, param in enumerate(grid.best_params_):\n",
    "        if (idx == param_to_vary_idx):\n",
    "            slices.append(slice(None))\n",
    "            continue\n",
    "        best_param_val = grid.best_params_[param]\n",
    "        idx_of_best_param = 0\n",
    "        if isinstance(param_ranges[idx], np.ndarray):\n",
    "            idx_of_best_param = param_ranges[idx].tolist().index(best_param_val)\n",
    "        else:\n",
    "            idx_of_best_param = param_ranges[idx].index(best_param_val)\n",
    "        slices.append(idx_of_best_param)\n",
    "\n",
    "    valid_scores_mean = valid_scores_mean[tuple(slices)]\n",
    "    valid_scores_std = valid_scores_std[tuple(slices)]\n",
    "\n",
    "    plt.clf()\n",
    "    plt.title(title)\n",
    "    plt.xlabel(param_to_vary)\n",
    "    plt.ylabel('Score')\n",
    "\n",
    "    if (ylim is None):\n",
    "        plt.ylim(0.0, 1.1)\n",
    "    else:\n",
    "        plt.ylim(*ylim)\n",
    "    if (not (xlim is None)):\n",
    "        plt.xlim(*xlim)\n",
    "        \n",
    "    lw = 2\n",
    "    plot_fn = plt.plot\n",
    "    if log:\n",
    "        plot_fn = plt.semilogx\n",
    "\n",
    "    param_range = param_ranges[param_to_vary_idx]\n",
    "    if (not isinstance(param_range[0], numbers.Number)):\n",
    "        param_range = [str(x) for x in param_range]\n",
    "    plot_fn(param_range, valid_scores_mean, label='Cross-Validation Accuracy',\n",
    "            color='b', lw=lw)\n",
    "    plt.fill_between(param_range, valid_scores_mean - valid_scores_std,\n",
    "                     valid_scores_mean + valid_scores_std, alpha=0.1,\n",
    "                     color='b', lw=lw)\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_grid_search_validation_curve(model, 'n_estimators', log=True, ylim=(.88, 1.02))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Complete Random Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 80 candidates, totalling 320 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    4.5s\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:   28.8s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  1.2min\n",
      "[Parallel(n_jobs=-1)]: Done 280 tasks      | elapsed:  2.3min\n",
      "[Parallel(n_jobs=-1)]: Done 320 out of 320 | elapsed:  2.7min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 500,\n",
       " 'min_samples_split': 7,\n",
       " 'min_samples_leaf': 4,\n",
       " 'max_features': 'log2',\n",
       " 'max_depth': 142,\n",
       " 'criterion': 'entropy'}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_search = {'criterion': ['entropy', 'gini'],\n",
    "               'max_depth': list(np.linspace(10, 1200, 10, dtype = int)) + [None],\n",
    "               'max_features': ['auto', 'sqrt','log2', None],\n",
    "               'min_samples_leaf': [4, 6, 8, 12],\n",
    "               'min_samples_split': [5, 7, 10, 14],\n",
    "               'n_estimators': list(np.linspace(151, 1200, 10, dtype = int))}\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "model = RandomizedSearchCV(estimator = clf, param_distributions = random_search, n_iter = 80, \n",
    "                               cv = 4, verbose= 5, random_state= 101, n_jobs = -1)\n",
    "model.fit(X_Train,Y_Train)\n",
    "\n",
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbded5a85c0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = pd.pivot_table(pd.DataFrame(model.cv_results_),\n",
    "    values='mean_test_score', index='param_max_depth', columns='param_min_samples_split')\n",
    "     \n",
    "sns.heatmap(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbded3560b8>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = pd.pivot_table(pd.DataFrame(model.cv_results_),\n",
    "    values='mean_test_score', index='param_max_depth', columns='param_min_samples_leaf')\n",
    "     \n",
    "sns.heatmap(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fbded2d5208>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "table = pd.pivot_table(pd.DataFrame(model.cv_results_),\n",
    "    values='mean_test_score', index='param_n_estimators', columns='param_criterion')\n",
    "     \n",
    "sns.heatmap(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[115   1]\n",
      " [  6 118]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      0.99      0.97       116\n",
      "           1       0.99      0.95      0.97       124\n",
      "\n",
      "    accuracy                           0.97       240\n",
      "   macro avg       0.97      0.97      0.97       240\n",
      "weighted avg       0.97      0.97      0.97       240\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictionforest = model.best_estimator_.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))\n",
    "acc3 = accuracy_score(Y_Test,predictionforest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotly Interactive Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This interactive data visualization was realised following Xoel López Barata\n",
    "guide: https://towardsdatascience.com/using-3d-visualizations-to-tune-hyperparameters-of-ml-models-with-python-ba2885eab2e9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_criterion</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.707879</td>\n",
       "      <td>0.005801</td>\n",
       "      <td>0.037025</td>\n",
       "      <td>0.001669</td>\n",
       "      <td>267.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>log2</td>\n",
       "      <td>1200</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'n_estimators': 267, 'min_samples_split': 14,...</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.942857</td>\n",
       "      <td>0.914286</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.010102</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.803975</td>\n",
       "      <td>0.018831</td>\n",
       "      <td>0.124854</td>\n",
       "      <td>0.005802</td>\n",
       "      <td>966.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>274</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 966, 'min_samples_split': 10,...</td>\n",
       "      <td>0.921429</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>0.921429</td>\n",
       "      <td>0.935714</td>\n",
       "      <td>0.933929</td>\n",
       "      <td>0.014617</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.790679</td>\n",
       "      <td>0.019953</td>\n",
       "      <td>0.037929</td>\n",
       "      <td>0.002827</td>\n",
       "      <td>267.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>auto</td>\n",
       "      <td>274</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 267, 'min_samples_split': 5, ...</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>0.921429</td>\n",
       "      <td>0.935714</td>\n",
       "      <td>0.935714</td>\n",
       "      <td>0.013363</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.288275</td>\n",
       "      <td>0.035759</td>\n",
       "      <td>0.114665</td>\n",
       "      <td>0.004402</td>\n",
       "      <td>850.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>log2</td>\n",
       "      <td>142</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'n_estimators': 850, 'min_samples_split': 5, ...</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.933929</td>\n",
       "      <td>0.009279</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.463521</td>\n",
       "      <td>0.052918</td>\n",
       "      <td>0.089374</td>\n",
       "      <td>0.005642</td>\n",
       "      <td>733.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>None</td>\n",
       "      <td>1067</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 733, 'min_samples_split': 7, ...</td>\n",
       "      <td>0.907143</td>\n",
       "      <td>0.957143</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.930357</td>\n",
       "      <td>0.017768</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0       0.707879      0.005801         0.037025        0.001669   \n",
       "1       2.803975      0.018831         0.124854        0.005802   \n",
       "2       0.790679      0.019953         0.037929        0.002827   \n",
       "3       2.288275      0.035759         0.114665        0.004402   \n",
       "4       2.463521      0.052918         0.089374        0.005642   \n",
       "\n",
       "   param_n_estimators  param_min_samples_split  param_min_samples_leaf  \\\n",
       "0               267.0                     14.0                     4.0   \n",
       "1               966.0                     10.0                     8.0   \n",
       "2               267.0                      5.0                     8.0   \n",
       "3               850.0                      5.0                     4.0   \n",
       "4               733.0                      7.0                     8.0   \n",
       "\n",
       "  param_max_features param_max_depth param_criterion  \\\n",
       "0               log2            1200            gini   \n",
       "1               sqrt             274         entropy   \n",
       "2               auto             274         entropy   \n",
       "3               log2             142            gini   \n",
       "4               None            1067         entropy   \n",
       "\n",
       "                                              params  split0_test_score  \\\n",
       "0  {'n_estimators': 267, 'min_samples_split': 14,...           0.928571   \n",
       "1  {'n_estimators': 966, 'min_samples_split': 10,...           0.921429   \n",
       "2  {'n_estimators': 267, 'min_samples_split': 5, ...           0.928571   \n",
       "3  {'n_estimators': 850, 'min_samples_split': 5, ...           0.928571   \n",
       "4  {'n_estimators': 733, 'min_samples_split': 7, ...           0.907143   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  mean_test_score  \\\n",
       "0           0.942857           0.914286           0.928571         0.928571   \n",
       "1           0.957143           0.921429           0.935714         0.933929   \n",
       "2           0.957143           0.921429           0.935714         0.935714   \n",
       "3           0.950000           0.928571           0.928571         0.933929   \n",
       "4           0.957143           0.928571           0.928571         0.930357   \n",
       "\n",
       "   std_test_score  rank_test_score  \n",
       "0        0.010102               61  \n",
       "1        0.014617               14  \n",
       "2        0.013363                6  \n",
       "3        0.009279               14  \n",
       "4        0.017768               51  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame.from_dict(model.cv_results_)\n",
    "df['param_min_samples_leaf'] = df['param_min_samples_leaf'].astype(float)\n",
    "df['param_n_estimators'] = df['param_n_estimators'].astype(float)\n",
    "df['param_min_samples_split'] = df['param_min_samples_split'].astype(float)\n",
    "df['mean_fit_time'] = df['mean_fit_time'].astype(float)\n",
    "df['mean_test_score'] = df['mean_test_score'].astype(float)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "text = list(\n",
    "    zip(\n",
    "        'max_features: ' + df['param_max_features'].apply(str),\n",
    "        'n_estimators: ' + df['param_n_estimators'].apply(str),\n",
    "        'min_samples_split: ' + df['param_min_samples_split'].apply(str),\n",
    "        'Test score: ' + df['mean_test_score'].round(3).apply(str),\n",
    "        'Training time: ' + (df['mean_fit_time'] % 60).round(3).apply(str) + ' s',\n",
    "    )\n",
    ")\n",
    "\n",
    "hower_text = ['<br>'.join(i) for i in text]\n",
    "df['hover_text'] = hower_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c081de3dd04e4990a5aceb649036b2ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(interactive(children=(FloatRangeSlider(value=(151.0, 1200.0), description='n_estimators', layou…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "from ipywidgets import interactive, VBox, widgets, interact\n",
    "\n",
    "trace = go.Scatter3d(\n",
    "    x= df['param_n_estimators'],\n",
    "    y= df['param_min_samples_leaf'],\n",
    "    z= df['param_min_samples_split'],\n",
    "    mode='markers', \n",
    "    marker=dict(\n",
    "        size= 7,\n",
    "        color= df['mean_test_score'],\n",
    "        colorscale= 'Jet',\n",
    "        colorbar= dict(title = 'Cross-Validation Accuracy')\n",
    "    ),\n",
    "    text= df['hover_text'],\n",
    "    hoverinfo= 'text'\n",
    ")\n",
    "\n",
    "data = [trace]\n",
    "layout = go.Layout(\n",
    "    scene = dict(\n",
    "        camera = dict(\n",
    "            up=dict(x=0, y=0, z=1),\n",
    "            center=dict(x=0, y=0, z=0),\n",
    "            eye=dict(x=2, y=2, z=1.25)\n",
    "        ),\n",
    "        xaxis = dict(\n",
    "            title='n_estimators',\n",
    "            range=[min(df['param_n_estimators']), max(df['param_n_estimators'])]\n",
    "        ),\n",
    "        yaxis = dict(\n",
    "            title='min_leaf',\n",
    "            type='log'\n",
    "        ),\n",
    "        zaxis = dict(\n",
    "            title='min_split',\n",
    "            type='log'\n",
    "\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "fig = go.FigureWidget(data,layout)\n",
    "\n",
    "xmin, xmax = fig['layout']['scene']['xaxis']['range']\n",
    "\n",
    "slider = widgets.FloatRangeSlider(\n",
    "    value= fig.layout.scene.xaxis.range,\n",
    "    min= xmin,\n",
    "    max= xmax,\n",
    "    step= (xmax - xmin) / 50,\n",
    "    description= 'n_estimators')\n",
    "slider.layout.width = '700px'\n",
    "\n",
    "def update_range(y):\n",
    "    fig.layout.scene.xaxis.range = [y[0], y[1]]\n",
    "    \n",
    "box = VBox((interactive(update_range, y=slider), fig))\n",
    "box.layout.align_items = 'center'\n",
    "box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65c9385adad947a98e72cbb33f5f3c35",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'hoverinfo': 'text',\n",
       "              'marker': {'color': array([0.92857143, 0.93392…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "listn = list(set(df['param_n_estimators']))\n",
    "listn.sort()\n",
    "\n",
    "data = []\n",
    "for i, n in enumerate(listn):\n",
    "    filtered_df = df[df.param_n_estimators==n]\n",
    "    trace = [\n",
    "        go.Scatter3d(\n",
    "    x= filtered_df['param_n_estimators'],\n",
    "    y= filtered_df['param_min_samples_leaf'],\n",
    "    z= filtered_df['param_min_samples_split'],\n",
    "    mode='markers', \n",
    "    marker=dict(\n",
    "        size= 7,\n",
    "        color= df['mean_test_score'],\n",
    "        colorscale= 'Jet',\n",
    "        colorbar= dict(title = 'Cross-Validation Accuracy')\n",
    "    ),\n",
    "    text= filtered_df['hover_text'],\n",
    "    hoverinfo= 'text'\n",
    ")\n",
    "    ]\n",
    "    \n",
    "    data.append(trace[0])\n",
    "    data[i].showlegend=False\n",
    "\n",
    "steps = []\n",
    "for i, n in enumerate(listn):\n",
    "    step = dict(\n",
    "        method='restyle',\n",
    "        args = ['visible', [False] * len(data) * 2]\n",
    "    )\n",
    "    step['args'][1][i] = True # toggle i'th traces to 'visible'\n",
    "    step['label'] = str(n)\n",
    "    steps.append(step)\n",
    "\n",
    "\n",
    "\n",
    "sliders = [dict(\n",
    "    active = 4,\n",
    "    currentvalue = {\"prefix\": \"n_estimators: \"},\n",
    "    pad = {\"t\": 10, 'b': 20},\n",
    "    steps = steps,\n",
    "    len=.5,\n",
    "    xanchor = 'center',\n",
    "    x = 0.5\n",
    ")]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = \"Cross-Validation Accuracy Varying Hyperparameters\",\n",
    "    width=700,\n",
    "    height=600,\n",
    "    sliders = sliders,\n",
    "    scene = dict(\n",
    "        camera = dict(\n",
    "            up=dict(x=0, y=0, z=1),\n",
    "            center=dict(x=0, y=0, z=0),\n",
    "            eye=dict(x=2, y=2, z=1.25)\n",
    "        ),\n",
    "        xaxis = dict(\n",
    "            title='n_estimators',\n",
    "        ),\n",
    "        yaxis = dict(\n",
    "            title='min_leaf',\n",
    "            type='log'\n",
    "        ),\n",
    "        zaxis = dict(\n",
    "            title='min_split',\n",
    "            type='log'\n",
    "\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "\n",
    "fig = go.FigureWidget(data,layout)\n",
    "fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a3fdefd1ac64511b458a098c037a845",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FigureWidget({\n",
       "    'data': [{'hoverinfo': 'text',\n",
       "              'marker': {'color': array([0.92857143, 0.93392…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = []\n",
    "for i, n in enumerate(listn):\n",
    "    filtered_df = df[df.param_n_estimators==n]\n",
    "    trace = [\n",
    "        go.Scatter3d(\n",
    "    x= filtered_df['param_n_estimators'],\n",
    "    y= filtered_df['param_min_samples_leaf'],\n",
    "    z= filtered_df['mean_test_score'],\n",
    "    mode='markers', \n",
    "    marker=dict(\n",
    "        size= 7,\n",
    "        color= df['mean_test_score'],\n",
    "        colorscale= 'Jet',\n",
    "    ),\n",
    "    text= filtered_df['hover_text'],\n",
    "    hoverinfo= 'text'\n",
    ")\n",
    "    ]\n",
    "    \n",
    "    data.append(trace[0])\n",
    "    data[i].showlegend=False\n",
    "\n",
    "steps = []\n",
    "for i, n in enumerate(listn):\n",
    "    step = dict(\n",
    "        method='restyle',\n",
    "        args = ['visible', [False] * len(data) * 2]\n",
    "    )\n",
    "    step['args'][1][i] = True # toggle i'th traces to 'visible'\n",
    "    step['label'] = str(n)\n",
    "    steps.append(step)\n",
    "\n",
    "\n",
    "\n",
    "sliders = [dict(\n",
    "    active = 4,\n",
    "    currentvalue = {\"prefix\": \"n_estimators: \"},\n",
    "    pad = {\"t\": 10, 'b': 20},\n",
    "    steps = steps,\n",
    "    len=.5,\n",
    "    xanchor = 'center',\n",
    "    x = 0.5\n",
    ")]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title = \"Cross-Validation Accuracy Varying n_estimators and min_leaf\",\n",
    "    width=700,\n",
    "    height=600,\n",
    "    sliders = sliders,\n",
    "    scene = dict(\n",
    "        camera = dict(\n",
    "            up=dict(x=0, y=0, z=1),\n",
    "            center=dict(x=0, y=0, z=0),\n",
    "            eye=dict(x=2, y=2, z=1.25)\n",
    "        ),\n",
    "        xaxis = dict(\n",
    "            title='n_estimators',\n",
    "        ),\n",
    "        yaxis = dict(\n",
    "            title='min_leaf',\n",
    "            type='log'\n",
    "        ),\n",
    "        zaxis = dict(\n",
    "            title='accuracy',\n",
    "            type='log'\n",
    "\n",
    "        ),\n",
    "    ),\n",
    ")\n",
    "\n",
    "fig = go.FigureWidget(data,layout)\n",
    "fig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Grid Search, we set up a grid of hyperparameters and train/test our model on each of the possible combinations. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to choose the parameters to use in Grid Search, we can try to run first Random Search to get an understanding of which values might perform best on the considered dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': ['entropy'], 'max_depth': [142], 'max_features': ['log2'], 'min_samples_leaf': [2, 4, 6], 'min_samples_split': [4, 7, 10], 'n_estimators': [350, 400, 500, 600, 650]}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "grid_search = {\n",
    "    'criterion': [model.best_params_['criterion']],\n",
    "    'max_depth': [model.best_params_['max_depth']],\n",
    "    'max_features': [model.best_params_['max_features']],\n",
    "    'min_samples_leaf': [model.best_params_['min_samples_leaf'] - 2, \n",
    "                         model.best_params_['min_samples_leaf'], \n",
    "                         model.best_params_['min_samples_leaf'] + 2],\n",
    "    'min_samples_split': [model.best_params_['min_samples_split'] - 3, \n",
    "                          model.best_params_['min_samples_split'], \n",
    "                          model.best_params_['min_samples_split'] + 3],\n",
    "    'n_estimators': [model.best_params_['n_estimators'] - 150, model.best_params_['n_estimators'] - 100, \n",
    "                     model.best_params_['n_estimators'], \n",
    "                     model.best_params_['n_estimators'] + 100, model.best_params_['n_estimators'] + 150]\n",
    "}\n",
    "\n",
    "print(grid_search)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 4 folds for each of 45 candidates, totalling 180 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  10 tasks      | elapsed:    4.1s\n",
      "[Parallel(n_jobs=-1)]: Done  64 tasks      | elapsed:   26.4s\n",
      "[Parallel(n_jobs=-1)]: Done 154 tasks      | elapsed:  1.0min\n",
      "[Parallel(n_jobs=-1)]: Done 180 out of 180 | elapsed:  1.2min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, error_score='raise-deprecating',\n",
       "             estimator=RandomForestClassifier(bootstrap=True, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              n_estimators='warn', n_jobs=None,\n",
       "                                              oob_score=False,\n",
       "                                              random_state=None, verbose=0,\n",
       "                                              warm_start=False),\n",
       "             iid='warn', n_jobs=-1,\n",
       "             param_grid={'criterion': ['entropy'], 'max_depth': [142],\n",
       "                         'max_features': ['log2'],\n",
       "                         'min_samples_leaf': [2, 4, 6],\n",
       "                         'min_samples_split': [4, 7, 10],\n",
       "                         'n_estimators': [350, 400, 500, 600, 650]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=5)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = RandomForestClassifier()\n",
    "model = GridSearchCV(estimator = clf, param_grid = grid_search, \n",
    "                               cv = 4, verbose= 5, n_jobs = -1)\n",
    "model.fit(X_Train,Y_Train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'max_depth': 142,\n",
       " 'max_features': 'log2',\n",
       " 'min_samples_leaf': 2,\n",
       " 'min_samples_split': 10,\n",
       " 'n_estimators': 500}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[115   1]\n",
      " [  7 117]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.97       116\n",
      "           1       0.99      0.94      0.97       124\n",
      "\n",
      "    accuracy                           0.97       240\n",
      "   macro avg       0.97      0.97      0.97       240\n",
      "weighted avg       0.97      0.97      0.97       240\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictionforest = model.best_estimator_.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))\n",
    "acc4 = accuracy_score(Y_Test,predictionforest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Grid Search is slower compared to Random Search but can be more effective because it can go through the whole search space. Instead, Random Search can be faster fast, but might miss some important points in the search space."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automated Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using Automated Hyperparameter Tuning, the model hyperparameters to try are identified using techniques such as: Bayesian Optimization, Gradient Descent and Evolutionary Algorithms. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bayesian Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hyperopt is a Python library used to optimize Machine Learning models using Bayesian Optimization. Bayesian optimization uses probability to find the minimum of a function. The final aim is to find the input value to a function which can gives us the lowest possible output value. <br>\n",
    "\n",
    "Baysian optimization has been proved to be more efficient than random, grid or manual search. Bayesian Optimization can therefore lead to better performance in the testing phase and reduced optimization time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Hyperopt, Bayesian Optimization can be implemented giving 3 three main parameters to the function fmin. <br>\n",
    "\n",
    "- Objective Function = defines the loss function to minimize.\n",
    "- Domain Space = defines the range of input values to test (in Bayesian Optimization this space creates a probability distribution for each of the used Hyperparameters).\n",
    "- Optimization Algorithm = defines the search algorithm to use to select the best input values to use in each new iteration. \n",
    "\n",
    "Additionally, can also be defined in fmin the maximum number of evaluations to perform."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bayesian Optimization can reduce the number of search iterations by choosing the input values bearing in mind the past outcomes. In this way, we can concentrate our search from the beginning on values which are closer to our desired output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import hp, fmin, tpe, STATUS_OK, Trials\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "space = {'criterion': hp.choice('criterion', ['entropy', 'gini']),\n",
    "        'max_depth': hp.quniform('max_depth', 10, 1200, 10),\n",
    "        'max_features': hp.choice('max_features', ['auto', 'sqrt','log2', None]),\n",
    "        'min_samples_leaf': hp.uniform ('min_samples_leaf', 0, 0.5),\n",
    "        'min_samples_split' : hp.uniform ('min_samples_split', 0, 1),\n",
    "        'n_estimators' : hp.choice('n_estimators', [10, 50, 300, 750, 1200])\n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def objective(space):\n",
    "    model = RandomForestClassifier(criterion = space['criterion'], max_depth = space['max_depth'],\n",
    "                                 max_features = space['max_features'],\n",
    "                                 min_samples_leaf = space['min_samples_leaf'],\n",
    "                                 min_samples_split = space['min_samples_split'],\n",
    "                                 n_estimators = space['n_estimators'], \n",
    "                                 )\n",
    "    \n",
    "    accuracy = cross_val_score(model, X_Train, Y_Train, cv = 4).mean()\n",
    "\n",
    "    # We aim to maximize accuracy, therefore we return it as a negative value\n",
    "    return {'loss': -accuracy, 'status': STATUS_OK }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now run our Bayesian Optimizer using the fmin() function. A Trials() object is first created to make possible later to visualize later what was going on while the fmin() function was running (eg. how the loss function was changing and how to used Hyperparameters were changing)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 80/80 [03:07<00:00,  2.02s/it, best loss: -0.9339285714285713]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'criterion': 1,\n",
       " 'max_depth': 120.0,\n",
       " 'max_features': 2,\n",
       " 'min_samples_leaf': 0.0006380325074247448,\n",
       " 'min_samples_split': 0.06603114636418073,\n",
       " 'n_estimators': 1}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trials = Trials()\n",
    "best = fmin(fn= objective,\n",
    "            space= space,\n",
    "            algo= tpe.suggest,\n",
    "            max_evals = 80,\n",
    "            trials= trials)\n",
    "best"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 criterion\n",
      "1 max_depth\n",
      "2 max_features\n",
      "3 min_samples_leaf\n",
      "4 min_samples_split\n",
      "5 n_estimators\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# From: https://medium.com/district-data-labs/parameter-tuning-with-hyperopt-faa86acdfdce\n",
    "parameters = ['criterion', 'max_depth', 'max_features', 'min_samples_leaf', 'min_samples_split',\n",
    "              'n_estimators']\n",
    "f, axes = plt.subplots(nrows=3, ncols=2, figsize=(15,10))\n",
    "f.tight_layout()\n",
    "cmap = plt.cm.jet\n",
    "for i, val in enumerate(parameters):\n",
    "    print(i, val)\n",
    "    xs = np.array([t['misc']['vals'][val] for t in trials.trials]).ravel()\n",
    "    ys = [-t['result']['loss'] for t in trials.trials]\n",
    "    xs, ys = zip(*sorted(zip(xs, ys)))\n",
    "    ys = np.array(ys)\n",
    "    axes[i//2,i%2].scatter(xs, ys, s=20, linewidth=0.01, alpha=0.5, c=cmap(float(i)/len(parameters)))\n",
    "    axes[i//2,i%2].set_title(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gini\n",
      "log2\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "crit = {0: 'entropy', 1: 'gini'}\n",
    "feat = {0: 'auto', 1: 'sqrt', 2: 'log2', 3: None}\n",
    "est = {0: 10, 1: 50, 2: 300, 3: 750, 4: 1200}\n",
    "\n",
    "print(crit[best['criterion']])\n",
    "print(feat[best['max_features']])\n",
    "print(est[best['n_estimators']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[114   2]\n",
      " [ 11 113]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.98      0.95       116\n",
      "           1       0.98      0.91      0.95       124\n",
      "\n",
      "    accuracy                           0.95       240\n",
      "   macro avg       0.95      0.95      0.95       240\n",
      "weighted avg       0.95      0.95      0.95       240\n",
      "\n"
     ]
    }
   ],
   "source": [
    "trainedforest = RandomForestClassifier(criterion = crit[best['criterion']], max_depth = best['max_depth'], \n",
    "                                       max_features = feat[best['max_features']], \n",
    "                                       min_samples_leaf = best['min_samples_leaf'], \n",
    "                                       min_samples_split = best['min_samples_split'], \n",
    "                                       n_estimators = est[best['n_estimators']]).fit(X_Train,Y_Train)\n",
    "predictionforest = trainedforest.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))\n",
    "acc5 = accuracy_score(Y_Test,predictionforest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Genetic Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Genetic Algorithms tries to apply natural selection mechanisms to Machine Learning contexts. \n",
    "\n",
    "Let's immagine we create a population of N Machine Learning models with some predifined Hyperparameters. We can then calculate the accuracy of each model and decide to keep just half of the models (the ones that performs best). We can now generate some offsprings having similar Hyperparameters to the ones of the best models so that go get again a population of N models. At this point we can again caltulate the accuracy of each model and repeate the cycle for a defined number of generations. In this way, just the best models will survive at the end of the process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {'criterion': ['entropy', 'gini'],\n",
    "               'max_depth': list(np.linspace(10, 1200, 10, dtype = int)) + [None],\n",
    "               'max_features': ['auto', 'sqrt','log2', None],\n",
    "               'min_samples_leaf': [4, 12],\n",
    "               'min_samples_split': [5, 10],\n",
    "               'n_estimators': list(np.linspace(151, 1200, 10, dtype = int))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e137e74a5fbf44f5a344652df1758136",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Optimization Progress', max=84, style=ProgressStyle(descripti…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generation 1 - Current best internal CV score: 0.9392857142857143\n",
      "Generation 2 - Current best internal CV score: 0.9392857142857143\n",
      "Generation 3 - Current best internal CV score: 0.9392857142857143\n",
      "Generation 4 - Current best internal CV score: 0.9392857142857143\n",
      "Generation 5 - Current best internal CV score: 0.9392857142857143\n",
      "\r\n",
      "Best pipeline: RandomForestClassifier(CombineDFs(input_matrix, input_matrix), criterion=entropy, max_depth=406, max_features=log2, min_samples_leaf=4, min_samples_split=5, n_estimators=617)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "TPOTClassifier(config_dict={'sklearn.ensemble.RandomForestClassifier': {'criterion': ['entropy',\n",
       "                                                                                      'gini'],\n",
       "                                                                        'max_depth': [10,\n",
       "                                                                                      142,\n",
       "                                                                                      274,\n",
       "                                                                                      406,\n",
       "                                                                                      538,\n",
       "                                                                                      671,\n",
       "                                                                                      803,\n",
       "                                                                                      935,\n",
       "                                                                                      1067,\n",
       "                                                                                      1200,\n",
       "                                                                                      None],\n",
       "                                                                        'max_features': ['auto',\n",
       "                                                                                         'sqrt',\n",
       "                                                                                         'log2',\n",
       "                                                                                         None],\n",
       "                                                                        'min_samples_leaf': [4,\n",
       "                                                                                             12],\n",
       "                                                                        'min_samples_split': [5,\n",
       "                                                                                              10],\n",
       "                                                                        'n_estimators': [151,\n",
       "                                                                                         267,\n",
       "                                                                                         384,\n",
       "                                                                                         500,\n",
       "                                                                                         617,\n",
       "                                                                                         733,\n",
       "                                                                                         850,\n",
       "                                                                                         966,\n",
       "                                                                                         1083,\n",
       "                                                                                         1200]}},\n",
       "               crossover_rate=0.1, cv=4, disable_update_check=False,\n",
       "               early_stop=12, generations=5, max_eval_time_mins=5,\n",
       "               max_time_mins=None, memory=None, mutation_rate=0.9, n_jobs=1,\n",
       "               offspring_size=12, periodic_checkpoint_folder=None,\n",
       "               population_size=24, random_state=None, scoring='accuracy',\n",
       "               subsample=1.0, template=None, use_dask=False, verbosity=2,\n",
       "               warm_start=False)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tpot import TPOTClassifier\n",
    "from deap.gp import Primitive\n",
    "\n",
    "\n",
    "tpot_classifier = TPOTClassifier(generations= 5, population_size= 24, offspring_size= 12,\n",
    "                                 verbosity= 2, early_stop= 12,\n",
    "                                 config_dict={'sklearn.ensemble.RandomForestClassifier': parameters}, \n",
    "                                 cv = 4, scoring = 'accuracy')\n",
    "tpot_classifier.fit(X_Train,Y_Train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9708333333333333\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/sklearn/preprocessing/_function_transformer.py:97: FutureWarning:\n",
      "\n",
      "The default validate=True will be replaced by validate=False in 0.22.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/preprocessing/_function_transformer.py:97: FutureWarning:\n",
      "\n",
      "The default validate=True will be replaced by validate=False in 0.22.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "acc6 = tpot_classifier.score(X_Test, Y_Test)\n",
    "print(acc6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://medium.com/cindicator/genetic-algorithms-and-hyperparameters-weekend-of-a-data-scientist-8f069669015e\n",
    "args = {}\n",
    "for arg in tpot_classifier._optimized_pipeline:\n",
    "    if type(arg) != Primitive:\n",
    "        try:\n",
    "            if arg.value.split('__')[1].split('=')[0] in ['criterion', 'max_depth', \n",
    "                                                          'max_features', 'min_samples_leaf', \n",
    "                                                          'min_samples_split',\n",
    "                                                          'n_estimators']:\n",
    "                args[arg.value.split('__')[1].split('=')[0]] = int(arg.value.split('__')[1].split('=')[1])\n",
    "            else:\n",
    "                args[arg.value.split('__')[1].split('=')[0]] = float(arg.value.split('__')[1].split('=')[1])\n",
    "        except:\n",
    "            pass\n",
    "params = args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 406,\n",
       " 'min_samples_leaf': 4,\n",
       " 'min_samples_split': 5,\n",
       " 'n_estimators': 617}"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[115   1]\n",
      " [  7 117]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.94      0.99      0.97       116\n",
      "           1       0.99      0.94      0.97       124\n",
      "\n",
      "    accuracy                           0.97       240\n",
      "   macro avg       0.97      0.97      0.97       240\n",
      "weighted avg       0.97      0.97      0.97       240\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9666666666666667"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier( max_depth = params['max_depth'],\n",
    "                             min_samples_leaf = params['min_samples_leaf'],\n",
    "                             min_samples_split = params['min_samples_split'],\n",
    "                             n_estimators = params['n_estimators'], \n",
    "                             )\n",
    "model.fit(X_Train,Y_Train)\n",
    "predictionforest = model.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))\n",
    "accuracy_score(Y_Test,predictionforest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Artificial Neural Networks (ANNs) Tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using KerasClassifier wrapper, it is possible to apply Grid Search and Random Search for Deep Learning models in the same way it was done when using Sklearn Machine Learning models. More examples are available here:\n",
    "- https://machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning:\n",
      "\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning:\n",
      "\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning:\n",
      "\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning:\n",
      "\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning:\n",
      "\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning:\n",
      "\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning:\n",
      "\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning:\n",
      "\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning:\n",
      "\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning:\n",
      "\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning:\n",
      "\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning:\n",
      "\n",
      "Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning:\n",
      "\n",
      "The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/joblib/externals/loky/process_executor.py:706: UserWarning:\n",
      "\n",
      "A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "\n",
      "/opt/conda/lib/python3.6/site-packages/sklearn/model_selection/_search.py:814: DeprecationWarning:\n",
      "\n",
      "The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Accuracy Registred: 0.932 using {'activation': 'relu', 'neurons': 35, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.488 % using: {'activation': 'softmax', 'neurons': 5, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.887 % using: {'activation': 'softmax', 'neurons': 5, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.891 % using: {'activation': 'softmax', 'neurons': 5, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.455 % using: {'activation': 'softmax', 'neurons': 10, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.887 % using: {'activation': 'softmax', 'neurons': 10, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.879 % using: {'activation': 'softmax', 'neurons': 10, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.454 % using: {'activation': 'softmax', 'neurons': 15, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.887 % using: {'activation': 'softmax', 'neurons': 15, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.882 % using: {'activation': 'softmax', 'neurons': 15, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.459 % using: {'activation': 'softmax', 'neurons': 25, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.891 % using: {'activation': 'softmax', 'neurons': 25, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.882 % using: {'activation': 'softmax', 'neurons': 25, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.459 % using: {'activation': 'softmax', 'neurons': 35, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.889 % using: {'activation': 'softmax', 'neurons': 35, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.879 % using: {'activation': 'softmax', 'neurons': 35, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.459 % using: {'activation': 'softmax', 'neurons': 50, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.887 % using: {'activation': 'softmax', 'neurons': 50, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.88 % using: {'activation': 'softmax', 'neurons': 50, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.902 % using: {'activation': 'relu', 'neurons': 5, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.898 % using: {'activation': 'relu', 'neurons': 5, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.902 % using: {'activation': 'relu', 'neurons': 5, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.895 % using: {'activation': 'relu', 'neurons': 10, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.923 % using: {'activation': 'relu', 'neurons': 10, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.914 % using: {'activation': 'relu', 'neurons': 10, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.902 % using: {'activation': 'relu', 'neurons': 15, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.921 % using: {'activation': 'relu', 'neurons': 15, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.92 % using: {'activation': 'relu', 'neurons': 15, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.9 % using: {'activation': 'relu', 'neurons': 25, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.929 % using: {'activation': 'relu', 'neurons': 25, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.929 % using: {'activation': 'relu', 'neurons': 25, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.896 % using: {'activation': 'relu', 'neurons': 35, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.932 % using: {'activation': 'relu', 'neurons': 35, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.93 % using: {'activation': 'relu', 'neurons': 35, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.902 % using: {'activation': 'relu', 'neurons': 50, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.93 % using: {'activation': 'relu', 'neurons': 50, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.93 % using: {'activation': 'relu', 'neurons': 50, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.893 % using: {'activation': 'tanh', 'neurons': 5, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.909 % using: {'activation': 'tanh', 'neurons': 5, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.907 % using: {'activation': 'tanh', 'neurons': 5, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.905 % using: {'activation': 'tanh', 'neurons': 10, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.923 % using: {'activation': 'tanh', 'neurons': 10, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.921 % using: {'activation': 'tanh', 'neurons': 10, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.909 % using: {'activation': 'tanh', 'neurons': 15, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.925 % using: {'activation': 'tanh', 'neurons': 15, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.916 % using: {'activation': 'tanh', 'neurons': 15, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.905 % using: {'activation': 'tanh', 'neurons': 25, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.925 % using: {'activation': 'tanh', 'neurons': 25, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.927 % using: {'activation': 'tanh', 'neurons': 25, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.905 % using: {'activation': 'tanh', 'neurons': 35, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.929 % using: {'activation': 'tanh', 'neurons': 35, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.927 % using: {'activation': 'tanh', 'neurons': 35, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.907 % using: {'activation': 'tanh', 'neurons': 50, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.925 % using: {'activation': 'tanh', 'neurons': 50, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.927 % using: {'activation': 'tanh', 'neurons': 50, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.714 % using: {'activation': 'sigmoid', 'neurons': 5, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.882 % using: {'activation': 'sigmoid', 'neurons': 5, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.87 % using: {'activation': 'sigmoid', 'neurons': 5, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.63 % using: {'activation': 'sigmoid', 'neurons': 10, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.893 % using: {'activation': 'sigmoid', 'neurons': 10, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.891 % using: {'activation': 'sigmoid', 'neurons': 10, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.777 % using: {'activation': 'sigmoid', 'neurons': 15, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.896 % using: {'activation': 'sigmoid', 'neurons': 15, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.893 % using: {'activation': 'sigmoid', 'neurons': 15, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.825 % using: {'activation': 'sigmoid', 'neurons': 25, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.898 % using: {'activation': 'sigmoid', 'neurons': 25, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.898 % using: {'activation': 'sigmoid', 'neurons': 25, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.795 % using: {'activation': 'sigmoid', 'neurons': 35, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.9 % using: {'activation': 'sigmoid', 'neurons': 35, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.896 % using: {'activation': 'sigmoid', 'neurons': 35, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.705 % using: {'activation': 'sigmoid', 'neurons': 50, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.909 % using: {'activation': 'sigmoid', 'neurons': 50, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.902 % using: {'activation': 'sigmoid', 'neurons': 50, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.898 % using: {'activation': 'linear', 'neurons': 5, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.914 % using: {'activation': 'linear', 'neurons': 5, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.905 % using: {'activation': 'linear', 'neurons': 5, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.909 % using: {'activation': 'linear', 'neurons': 10, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.923 % using: {'activation': 'linear', 'neurons': 10, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.921 % using: {'activation': 'linear', 'neurons': 10, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.904 % using: {'activation': 'linear', 'neurons': 15, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.925 % using: {'activation': 'linear', 'neurons': 15, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.914 % using: {'activation': 'linear', 'neurons': 15, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.907 % using: {'activation': 'linear', 'neurons': 25, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.923 % using: {'activation': 'linear', 'neurons': 25, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.925 % using: {'activation': 'linear', 'neurons': 25, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.909 % using: {'activation': 'linear', 'neurons': 35, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.925 % using: {'activation': 'linear', 'neurons': 35, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.927 % using: {'activation': 'linear', 'neurons': 35, 'optimizer': 'Adamax'}\n",
      "Overall accuracy of 0.907 % using: {'activation': 'linear', 'neurons': 50, 'optimizer': 'SGD'}\n",
      "Overall accuracy of 0.927 % using: {'activation': 'linear', 'neurons': 50, 'optimizer': 'Adam'}\n",
      "Overall accuracy of 0.929 % using: {'activation': 'linear', 'neurons': 50, 'optimizer': 'Adamax'}\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "\n",
    "def DL_Model(activation= 'linear', neurons= 5, optimizer='Adam'):\n",
    "    model = Sequential()\n",
    "    model.add(Dense(neurons, input_dim= 4, activation= activation))\n",
    "    model.add(Dense(neurons, activation= activation))\n",
    "    model.add(Dropout(0.3))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    model.compile(loss='binary_crossentropy', optimizer= optimizer, metrics=['accuracy'])\n",
    "    return model\n",
    "\n",
    "# Definying grid parameters\n",
    "activation = ['softmax', 'relu', 'tanh', 'sigmoid', 'linear']\n",
    "neurons = [5, 10, 15, 25, 35, 50]\n",
    "optimizer = ['SGD', 'Adam', 'Adamax']\n",
    "param_grid = dict(activation = activation, neurons = neurons, optimizer = optimizer)\n",
    "\n",
    "clf = KerasClassifier(build_fn= DL_Model, epochs= 80, batch_size=40, verbose= 0)\n",
    "\n",
    "model = GridSearchCV(estimator= clf, param_grid=param_grid, n_jobs=-1)\n",
    "model.fit(X_Train,Y_Train)\n",
    "\n",
    "print(\"Max Accuracy Registred: {} using {}\".format(round(model.best_score_,3), model.best_params_))\n",
    "acc = model.cv_results_['mean_test_score']\n",
    "hyper = model.cv_results_['params']\n",
    "\n",
    "for mean, param in zip(acc, hyper):\n",
    "    print(\"Overall accuracy of {} % using: {}\".format(round(mean, 3), param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[115   1]\n",
      " [  8 116]]\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.93      0.99      0.96       116\n",
      "           1       0.99      0.94      0.96       124\n",
      "\n",
      "    accuracy                           0.96       240\n",
      "   macro avg       0.96      0.96      0.96       240\n",
      "weighted avg       0.96      0.96      0.96       240\n",
      "\n"
     ]
    }
   ],
   "source": [
    "predictionforest = model.predict(X_Test)\n",
    "print(confusion_matrix(Y_Test,predictionforest))\n",
    "print(classification_report(Y_Test,predictionforest))\n",
    "acc7 = accuracy_score(Y_Test,predictionforest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Base Accuracy vs Manual Search 0.0000%.\n",
      "Base Accuracy vs Random Search 2.1930%.\n",
      "Base Accuracy vs Grid Search 1.7544%.\n",
      "Base Accuracy vs Bayesian Optimization Accuracy -0.4386%.\n",
      "Base Accuracy vs Evolutionary Algorithms 2.1930%.\n",
      "Base Accuracy vs Optimized ANN 1.3158%.\n"
     ]
    }
   ],
   "source": [
    "print('Base Accuracy vs Manual Search {:0.4f}%.'.format( 100 * (acc2 - acc1) / acc1))\n",
    "print('Base Accuracy vs Random Search {:0.4f}%.'.format( 100 * (acc3 - acc1) / acc1))\n",
    "print('Base Accuracy vs Grid Search {:0.4f}%.'.format( 100 * (acc4 - acc1) / acc1))\n",
    "print('Base Accuracy vs Bayesian Optimization Accuracy {:0.4f}%.'.format( 100 * (acc5 - acc1) / acc1))\n",
    "print('Base Accuracy vs Evolutionary Algorithms {:0.4f}%.'.format( 100 * (acc6 - acc1) / acc1))\n",
    "print('Base Accuracy vs Optimized ANN {:0.4f}%.'.format( 100 * (acc7 - acc1) / acc1))"
   ]
  }
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